DFSMN-HMM [1]} based ASR model was adopted, which consists of 2 convolutional layers, 24 FSMN layers, a bottleneck layer and a feedforward layer.
39-dimensional MFCC feature was extracted using a 25 ms hamming window with 10 ms shift as the model input. While, the softmax output layer had 5,432 units, representing the senone labels derived from forced-alignment with a GMM-HMM system.
The deep features were extracted from output of the bottleneck layer with the dimension of 512. While, the phoneme GOP scores were calculated with frame-level senones and their corresponding posteriors. The frame accuracy of the ASR system is 73%.
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